AI-Powered Health Risk Prediction Models for SMEs in the Healthcare Sector: A Cost-Effective Approach for Developing Countries
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Keywords:
Artificial Intelligence, Health Risk Prediction, Small and Medium Enterprises (SMEs), Healthcare Innovation, Developing Countries, Cost-Effectiveness, Predictive Analytics, Digital TransformationAbstract
The healthcare sector in developing countries is increasingly facing pressure to improve
operational efficiency and patient outcomes, particularly among small and medium-sized enterprises (SMEs)
such as private clinics, rural health centers, and diagnostic labs. These entities often operate with limited
resources, yet they play a critical role in healthcare delivery. This study investigates the implementation of
artificial intelligence (AI)-powered health risk prediction models as a cost-effective solution for SMEs in
the healthcare sector. Using a mixed-methods approach, this research evaluates the economic viability,
predictive accuracy, and managerial usability of AI systems in identifying high-risk patients and preventing
costly medical complications. The findings demonstrate that AI models not only enhance clinical decision
making but also contribute to cost reductions and improved patient management—making them a viable
technological investment for resource-constrained healthcare SMEs. Additionally, the study highlights the
enabling role of digital infrastructure and data literacy in maximizing the benefits of AI adoption. The paper
concludes with strategic recommendations for policymakers and SME managers to accelerate AI
integration in healthcare ecosystems of developing countries.
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